Background: Adaptive radiation therapy (ART) can improve prognosis for nasopharyngeal carcinoma (NPC) patients. However, the inter-individual variability in anatomical changes, along with the resulting extension of treatment duration and increased workload for the radiologists, makes the selection of eligible patients a persistent challenge in clinical practice. The purpose of this study was to predict eligible ART candidates prior to radiation therapy (RT) for NPC patients using a classification neural network. By leveraging the fusion of medical imaging and clinical data, this method aimed to save time and resources in clinical workflows and improve treatment efficiency. Methods: We collected retrospective data from 305 NPC patients who received RT at Hong Kong Queen Elizabeth Hospital. Each patient sample included pre-treatment computed tomographic (CT) images, T1-weighted magnetic resonance imaging (MRI) data, and T2-weighted MRI images, along with clinical data. We developed and trained a novel multi-modal classification neural network that combines ResNet-50, cross-attention, multi-scale features, and clinical data for multi-modal fusion. The patients were categorized into two labels based on their re-plan status: patients who received ART during RT treatment, as determined by the radiation oncologist, and those who did not. Results: The experimental results demonstrated that the proposed multi-modal deep prediction model outperformed other commonly used deep learning networks, achieving an area under the curve (AUC) of 0.9070. These results indicated the ability of the model to accurately classify and predict ART eligibility for NPC patients. Conclusions: The proposed method showed good performance in predicting ART eligibility among NPC patients, highlighting its potential to enhance clinical decision-making, optimize treatment efficiency, and support more personalized cancer care.
背景：自适应放射治疗（ART）可改善鼻咽癌（NPC）患者的预后。然而，个体间解剖结构变化的差异性，以及由此导致的治疗周期延长和放射科医生工作量增加，使得在临床实践中筛选适合ART的患者始终面临挑战。本研究旨在通过分类神经网络，在鼻咽癌患者接受放射治疗（RT）前预测其是否适合接受ART。该方法通过融合医学影像与临床数据，以期节省临床工作流程的时间与资源，并提升治疗效率。方法：我们回顾性收集了305例在香港伊利沙伯医院接受放射治疗的鼻咽癌患者数据。每位患者的样本包括治疗前计算机断层扫描（CT）图像、T1加权磁共振成像（MRI）数据、T2加权MRI图像以及临床资料。我们开发并训练了一种新型多模态分类神经网络，该网络结合了ResNet-50、交叉注意力机制、多尺度特征以及临床数据进行多模态融合。根据患者在放疗期间是否由放射肿瘤科医生决定进行重新计划，将患者分为两类标签：接受ART治疗的患者与未接受ART治疗的患者。结果：实验结果表明，所提出的多模态深度预测模型优于其他常用深度学习网络，曲线下面积（AUC）达到0.9070。这些结果证明了该模型能够准确分类和预测鼻咽癌患者接受ART的适用性。结论：所提出的方法在预测鼻咽癌患者ART适用性方面表现出良好性能，凸显了其在辅助临床决策、优化治疗效率以及支持更个性化癌症治疗方面的潜力。
肿瘤(癌症)患者之家